Detecting literary elements in literature and their importance through semantic analysis and literary correlation

ABSTRACT

Automatic semantic analysis for characterizing and correlating literary elements within a digital work of literature is accomplished by employing natural language processing and deep semantic analysis of text to create annotations for the literary elements found in a segment or in the entirety of the literature, a weight to each literary element and its associated annotations, wherein the weight indicates an importance or relevance of a literary element to at least the segment of the work of literature; correlating and matching the literary elements to each other to establish one or more interrelationships; and producing an overall weight for the correlated matches.

CROSS-REFERENCE TO RELATED APPLICATIONS CLAIMING BENEFIT UNDER 35 U.S.C.120

This is a continuation application of U.S. patent application Ser. No.14/094,889, filed on Dec. 12, 2013, by Corville Orain Allen, et al.,which is currently under Notice of Allowance.

FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT STATEMENT

None.

MICROFICHE APPENDIX

Not applicable.

INCORPORATION BY REFERENCE

U.S. patent application Ser. No. 13/722,017, filed on Feb. 20, 2013, byCorville O. Allen, et al., is hereby incorporated by reference in itsentirety.

FIELD OF THE INVENTION

The invention generally relates to systems and methods for automaticallyextracting plot and character elements from works of literature for thepurposes of automating the summarization of literary elements and theirimportance to one or more users.

BACKGROUND OF INVENTION

In the context of the present disclosure and the related art, “works ofliterature” will be used to refer to textual writings which are readilyencoded into computer files, such as ASCII text, ANSI text, HypertextTransfer Markup Language (HTML), eXtensible Markup Language (XML),portable document format (PDF), word processor files (e.g. *.doc,*.docx, *.odt, *.wpd, etc.), ebook files and the like. The content ofthese files may represent digital novels, books, textbooks, referencebooks, poetry, lyrics, magazines, journals, short stories, catalogs,research papers, user manuals and the like, each of which may have astructural syntax such as a table of contents, and index, one or morechapters with one or more sections and subsections. These works will bereferred to collectively as “digital literature” for the purposes of thepresent disclosure.

Many online services which provide access to digital literature, such asonline book stores, online libraries and online research centers attemptto provide suggestions for similar literary works to users when theysearch for or purchase a particular literature item. Suchrecommendations can increase sales, improve customer affinity, and leadto better research of a subject matter.

SUMMARY OF EXEMPLARY EMBODIMENTS OF THE INVENTION

Automatic semantic analysis for characterizing and correlating literaryelements within a digital work of literature is accomplished byemploying natural language processing and deep semantic analysis of textto create annotations for the literary elements found in a segment or inthe entirety of the literature, a weight to each literary element andits associated annotations, wherein the weight indicates an importanceor relevance of a literary element to at least the segment of the workof literature; correlating and matching the literary elements to eachother to establish one or more interrelationships; and producing anoverall weight for the correlated matches.

BRIEF DESCRIPTION OF THE DRAWINGS

The description set forth herein is illustrated by the several drawings.

FIG. 1 illustrates an overall logical process according to the presentinvention.

FIG. 2 provides additional details of a logical process according to thepresent invention for building a multi-layer abstraction model of theanalyzed literary work.

FIG. 3 illustrates a generalized computing platform suitable forcombination with program instructions to perform a logical process suchas shown in FIG. 2 to yield a computer system embodiment according tothe present invention.

FIG. 4 depicts an arrangement of components and functions in whichembodiments of the present invention will find utility.

DETAILED DESCRIPTION OF EXEMPLARY EMBODIMENT(S) OF THE INVENTION

The inventors of the present and the related invention have recognizedproblems not yet recognized by those skilled in the relevant arts.Today, analysis and comparison of two or more digital literary pieces is“shallow” and largely based on high level concepts, genres, or main plotelements. For example, a person that likes one suspense/thrillerliterary work is considered to be likely to be interested in othersuspense/thriller literary works simply because they belong to the samegenre of literature.

So, by “shallow”, we mean analysis and subsequent comparison ofliterature pieces in the present art typically only extends to one levelof analysis, namely genre. Thus, comparison of two pieces of literatureaccording to shallow semantic analysis will also be shallow. Inaddition, analysis of plot elements within each work of literaturerequires considerable human effort to understand and comprehend theconcepts of interest that make literary pieces similar to one another.

The present inventors have recognized these problems of manually-drivencomparisons of literature, and, having reviewed the available technologyto address the problem, have found no suitable, useful solutions. Thus,the present invention was developed, in conjunction with the relatedinvention which is partially disclosed herein as a utility example ofembodiments of the present invention.

The inventors further realized that the present-day methodology alsofails to consider the nuances or deep semantics that make two literarypieces similar both conceptually and in writing style. And, in furtherconsideration of the virtual explosion of digitally published pieces ofliterature, including digital books (eBooks), digital sheet music, webpages, blogs, self-published books, etc., the task of manually reading,comprehending, analyzing and comparing digital literature is rapidlybecoming unwieldy, while customers of online digital literature expectinstant access to similar works with little or no human input or waittime.

The invention disclosed herein addresses the current problems as well asaddresses the future demands of such online systems for accessing,distributing, and purchasing digital literature.

Following the disclosure of illustrative embodiments of the presentinvention and that of the related invention, discussions of the reviewedavailable technologies and a comparisons to the presently-disclosedmethods and systems are provided.

Literary Terminology

Terminology used in analysis and criticism of works of literature can bechallenging to define in a precise manner. Many commonly used terms aresubject to debate by scholars in this field, such as the precise meaningof “plot” or “character”. Kathleen Morner and Ralph Rausch have statedin the forward of the NTC's Dictionary of Literary Terms (1998,McGraw-Hill) that such an effort to ascertain the exact meaning of aliterary term can be a “vicious circle”, wherein terms can be definedwith respect to each other. Thus, literary terminology iscontext-dependent to a significant degree. The terminology used in thepresent disclosure will be used in a manner consistent with automatedanalysis of works of text, and in a manner which lends itself tocomputer and software design.

Deep Semantic Analysis of Natural Language Text in General

The term “deep semantic” relationships, for the purposes of the presentdisclosure, is meant to refer to relationships between informationentities in a given context and how they relate to each other. They canbe the occurrence of triple store terms or entities or they can be theoccurrence with a relationship of those entities. For example,(Mutation, Cancer, Organ) would be a semantic relationship, identifyingthat mutations, cancer and specific organ ontologies have a deeprelationship. Further, a deep semantic analysis system sometimesassociates a specific relationship (mass, ?indicates, metastasis), wherethe combination and synonyms for “indicates” would mean the cancer hasmetastasized.

The term deep semantic relationship may also refer to the relationshipof terms in a specific ontology and their similarity when expressed inpassages of text based on the how they are typically expressed usingsequence matching algorithms for text analysis. For example, thewell-known Smith-Waterman sequence-matching algorithm measures thelengths of the longest similar subsequence between two texts, which isthen a measured or detected semantic relationship between those texts.

Deep semantic relationships consider the meaning of words within thecontext and structure of a sentence. They signify a “deep” understandingthe meaning of words that comprise a relationship within the sentence.Deep semantic relationships are usually developed with a very specificuse case in mind. For example, consider the sentence “John bought breadat the store.” From this, a relationship like sold (store, bread) may bemined, indicating that the store sold bread. This relationship requiresa deep understanding of what a store is (a retailer that sellsconsumable goods) and that bread is one of those items.

For example, one “specific use” in which deep semantic analysis has beenproposed is the deep semantic interpretations of legal texts as proposedby L. Thorne McCarty of Rutgers University (Association of ComputerMachinery (ACM), 971-1-59593-680). Another useful publicly-availabledocument regarding realization of a general purpose automatic deepsemantic analyzer of natural language text is described in “DeepSemantic Analysis of Text” by James F. Allen, et al., of the Universityof Rochester and the Institute for Human and Machine Cognition (documentW08-0227 from the ACL).

So, while deep semantic analysis of natural language text in general hasbeen discussed in the public domain, the inventors have discovered thatthe aforementioned problem of making an automated analysis of one ormore works of literature, those presently engaged in the art appear tobe focused on keyword searching and relevance ranking according tokeywords. One approach to advancing beyond keyword searching is“intent-centric” processing as proposed by Scott Brave, et al., in WIPOpatent application WO 2009/021198 A1. Inventors do not believe thisapproach, however solves the present problem because it addresses adifferent problem using a different approach without employing deepsemantic analysis.

The present invention is set forth in at least one exemplary embodimentas an application of or manner of using a deep semantic analyzerplatform. This platform may be a system such as the IBM Watson™ system,such as is described in “Building Watson: An Overview of the DeepQAProject” (Stanford University online, and AI Magazine, Fall 2010 issue).The foundation deep semantic analysis platform may be an alternategeneral-purpose deep semantic analyzer implementation such as thesemantic extraction component of the system described by Anna Stavrianouin United States Pre-Grant Published Patent Application 2013/0218914 A1(Aug. 22, 2013) suitably modified to include the functionality of therelated, incorporated patent application and that described herein bythe present inventors. Other useful, publicly-available teachingsregarding the availability of general purpose deep semantic analyzerswhich may be suitable for adapting and improving to the presentinvention may include those described by Konstantin Zuev in UnitedStates Pre-Grant Published Patent Application 2011/0270607 A1 (Nov. 3,2011); the Thompson's Motif-Index Literature system of Thiery Declerk,et al., as published in “Research and Advanced Technology for DigitalLibraries: Lecture Notes in Computer Science”, vol. 6966, 2011, pp.151-158; and using natural language parsers such as that described bySala Ait-Mokhtar, et al., in U.S. Pat. No. 7,058,567 (Jun. 6, 2006).

One may contrast deep semantic relationships with shallow semanticrelationships, that latter of which usually only consider the structureof parts of speech within a sentence, and not necessarily the meaningsof those words. An example shallow relationship may simply be of theform sentence (subject, verb, object). In the above example, this wouldbe sentence (john, bought, bread). These terms don't signify any specialmeaning, but their parts of speech form a shallow relationship called“sentence”.

Graphical logical forms for representation of text can be created usingone of several known methods, such as that proposed by James F. Allen,Mary Swift, and Will de Beaumont, of the University of Rochester and theInstitute for Human and Machine Cognition (Association for ComputerLinguistics (ACL), anthology document W08-2227).

Deep Semantic Analysis of Digital Works of Literature to Solve thePresently Addressed Problem

The present inventors have recognized that currently there appears to beno effective technology to conveniently and easily know what type ofcontent is contained in a digital work of literature, such as a novel, abook or an article) and the way in which certain literary elements flowthroughout the work of literature. From the designation of its genre,one may glean a general understanding of its overall subject matter,such as science fiction, suspense, or drama, and one may further get ageneral understanding of its plot and character content based on otherusers' reviews about the work of literature. But, one may only rarelyget to a deeper understanding of the writing style within the work ofliterature, or of the literary elements within the storyline.

For example, one may not easily or often gain an understanding of degreeof a characteristic in a work of literature, such as “how humorous”,“how descriptive”, and “how descriptive” certain elements are presented(e.g. settings, characters, locations, foods, mind-sets, etc.). Furtherto these shortcomings in the art, the present inventors have recognizedthat there doesn't seem to be technology to assist in quickly gauginghow these elements play out in segments in the work of literature, andwhether the literary elements are prevalent throughout the work ofliterature and how or if these elements would match one's own readinginterests.

The present inventors have recognized that current art only providesonline reviews written by other readers as the only reasonable way toget somewhat accurate understanding of a book, such as Cliff Notes™, NYTimes™ reviews, or other readers' reviews. Typically, one must read theseveral available reviews, and manually collate the reviews by pullinginformation from the other readers. None of these sources of review haveeasy ways to discern this information by processing the book content insome automated or reliable fashion.

Upon recognition of these unmet needs in the art and these problems, thepresent inventors have set out to develop a tool which allows a person(or another system) to get a representation and information of theliterary elements, tone, flow and content type of a work of literature,including, preferably, indications of where or how these elements mayfluctuate throughout the work of literature, and how prevalent eachelement is in the work of literature.

Such a tool would be useful to assist people to better understand whatkind of literature a particular work likely is, and the how much certaintypes of literary elements make up the book or other work of literature.

Overview of the Processes According to the Present Invention

A fundamental operation of the present invention is to detect, identifyand categorize various narrative characteristics within a digital workof literature by analyzing and capturing the literary elements itcontains, the levels of detail and likely interesting categories (fromthe perspective of a particular user) of data found in the work ofliterature. The generalized logical processes yield a literary metadatascale built from the analysis of the text across plot themes, with orwithout categories, to identify key literary elements and their impacton the overall literature through their consistency, occurrence andimportance to the literary art.

Further, with respect to analysis of a novel, the literary elements andthe metadata may comprise, but are not limited to, elements such ashumor, suspense, adventure, detailed description of setting (imagery),character mood and a measure or degree of how prevalently these elementspermeate throughout the work of literature, as well as a measurement ofthe ebb and flow of these elements in the plot by weight value. Theprevalence degree is determined by the detected relevance of eachliterary element, especially relative to their deep relationships to thedetected main characters and plot points within the story.

The identification of plot devices, such as introduction of characters,major events (death, celebration, marriage, war, etc.) and theirrelative importance and re-occurrence throughout the work of literatureis also analyzed in order to provide information on how and when variousparts of the storyline are advanced.

Still further, when it comes to literary works such as articles (e.g.medical journal, experiments and evidence, etc.), the literary elementcategories are flexible in such that they are used to identify keycategories, and their relevance to the type of article and theiroccurrence and importance throughout the literature.

One particular advantage of using a tool such as those described hereinis providing a user an ability to clearly identify, relate and show theimportance of these literary elements within the work of literature inorder for the user to easily and conveniently see the make-up in generalof a book or novel, to know where those elements exist, and tounderstand the strength of those elements in relation to the literatureand category.

Another advantage of using a tool such as described herein is that byusing this information, publishers and authors may also gain an improvedunderstanding of the make-up of a work of literature that they arewriting or publishing so that they may make adjustments to the contentto suit their potential readers interests better. This data set can beused in various ways in the literary field for publishers, authors,visualization tools, comparisons, identification of for those elements.

Such a tool can be offered as a service that consumers, publishers,authors and book-sellers can use to improve general access to bookcharacteristics, reader interests and to be able to make smartrecommendations for users and potential literature consumers.

Current Invention's Utility in a Larger Architecture

The invention disclosed herein and several related inventions, alsodisclosed in U.S. patent applications by the same inventors, optionallyfit within a larger architecture for literature analysis, recommendationand annotation. Turning to FIG. 4, such an arrangement of components andelements is shown. A work of literature under consideration (902) andoptionally one or more other works of literature (901) may be subjectedto deep semantic analysis to extract characters, their relationships toeach other and plot events, as well as other literary elements such aselements (and significance or intensity) of humor, mystery, drama,scenery, etc. One such automated deep semantic analysis process isdescribed in a related patent application by the present inventors.

Meta-data representing the results of this analysis is stored (904), andoptionally aggregated (905) with metadata which is converted frommanually-created descriptions of the works of literature, such asreviews, Cliff™ notes, condensed versions of the works, etc.

In one manner of using this metadata, which is disclosed in anotherrelated patent application, the meta-data may be further analyzed andorganized (906) into hierarchical layers of abstraction to allow readycomparison with other works of literature via their abstracted metadatarepresentations.

In another manner of using this metadata (904) which is disclosed inanother related patent application, the computer-generated meta-data, orthe meta-data converted from manually-generated reviews, or acombination (905) of both, is analyzed to generate (909) a visualizationof the work of literature (910) which relates segments of the literatureto plot events and intensity or significance of the literary elements(humor, mystery, scenery, etc.). This visualization (910) may then beused to annotate the work of literature (902) that it represents, suchas printing it on the back cover of the paper book or displaying itrelative to the digital book on a web page.

Another related invention for which another patent is pending by thesame inventors involves another use of this metadata, and optionallyuses the abstracted modeling process (disclosed in another relatedpatent application). According to embodiments of this related invention,the meta-data and models may be used by an inferential engine (907) todiscover deep similarities between two or more works of literature, andto yield one or more recommendations (908) to a potential consumer. Thepotential consumer's preferences may also optionally be factored intothe inferential engine's analysis, as is disclosed in the related patentapplication.

Structured Annotations

For the following logical processes, the term “structured annotations”will refer to at least one available embodiment of metadata (data aboutdata), such as the annotations generated by the present invention of thefollowing paragraphs. According to this exemplary embodiment, thestructure annotation constitutes information regarding where that datacan be found in a given passage or text, and contains the raw value ofthe selected text, keywords or terms. It usually has a preferred orinterpreted value for the term, usually also contains further metadatadescribing that term in a specific context. For example, the text“Peter” may be designated as an annotation with metadata: Noun—parts ofspeech, Lead Character, Brother to Kayla.

Another example, the text “Sam and Sarah felt anguish over the loss ofthe wood” may be denoted as the raw value of an annotation, withmetadata “Sadness”, where the term “sadness” is derived from the deepsemanatic analysis of the text to not only parse the phrase forstructure, but also determine the meaning of the words.

Logical Processes According to the Present Invention

The present invention may be realized as a processor executing certainprogram instructions (program code), or as a customized electroniccircuit device, or as a combination of processor, instructions, andcustom circuits.

Turning to FIG. 1, such a logical process (150) is shown, in which thereare three main components:

-   -   (1) a Literary Element Analyzer (151);    -   (2) a Literary Element Data Capturer (153); and    -   (3) a Literary Element Correlation Matcher (156).

The Literary Element Analyzer preferably receives one or more digitalworks of literature (200′), and employs natural language processing anddeep semantic processing and a type system around literary terms andcorpora that identifies categories of literary terms and annotators tomatch, identify and annotate (152) the literary elements withinformation.

The Literary Element Data Capturer attaches and augments the resultingannotations (152) of the Literary Element Analyzer by assigning (155) abasic level and weight normalized (154) by scale of the segment of thework of literature (e.g. by chapter, by passage, etc.).

The Literary Element Correlation Matcher receives the Literary ElementAnnotations, analyzes and correlates (158) them to create (157) plotdevices (152′) and character associations. Then, the Literary ElementCorrelation Matcher augments character elements regarding shifts in theplot (e.g. at plot event and plot devices), including identifying plotthemes (159) based on the frequency and scaling of the themes, andaugments the annotations and based on their occurrences, weights andimportance levels. Next, the Literary Element Correlation Matcherassigns an overall weight (160) by combining the set of annotationsaround that specific annotation and its metadata.

The information annotated is specific to the set of literary elementsrequired in that analysis, whether it's a novel or evidence foroncology, etc. For example, in analysis of a novel, one embodiment fordetecting humor in a chapter may proceed as follows:

-   -   (1) annotate and identify words associated with humor, such as        laugh, guffaws, smiled/smiling, grin, and certain key        relationships such as “corner of mouth turns up” into a synonym        table and mapping to a language ware based model for humor (e.g.        these associated words and terms may be stored in a thesaurus,        sorted or indexed by their more general terms, such as humor        (laugh, guffaw, grin, smile, chuckle, . . . ) and anger (yell,        scream, grimmace, scowl, furious, angry, mad, put_off,        irritated, . . . ));    -   (2) calculate a confidence level and a weight towards how much        humor exists by the number of participants, and the type of        writing style used to determine and how strong the language was        (e.g. a smile may be considered a subtle humor, while a guffaw        would carry more weight, such as by adding strength values to        each entry in the thesaurus, e.g. humor (bursts_out (10), laugh        (7), guffaw (9), grin (2), smile (2), chuckle (3), . . . )); and    -   (3) modifying each literary element annotation for humor with        metadata by the Literary Element Data (metadata) Capturer, such        as the following embodiment shown in eXentisible Markup Language        or XML):

<humor_element> Humor_Annotation   <humor_tone> 1:10 </humor_tone>  <humor_type> Malapropism:Pun:Polemics:. ..     </humor_type>  <humor_relationships>       character1:character2:...    </humor_relationships>   <humor_weight> 1:10 </humor_weight></humor_element>

For example, a method to detect a malapropism type of humor that wouldoperate on the following except sample text:

-   -   John says, “Texas has a lot of electrical votes!”    -   Mark looks at him and bursts out laughing.    -   John says, “Oh, I mean electoral votes”.

First, deep semantic relationship would detect a humor element(laughing) with a strong weight (bursts out), as well as therelationship to two characters (John and Mark). To identify the type ofhumor element, the analysis back traces and analyzes the subset of textprior to the “Mark laughs” by paragraph(s) and after “Mark Laughs” forentire paragraph, and by performing fact check Noun, possessive verb,and subject. A correction (electoral substituted for electrical) by anyof the participating characters denotes malapropism.

Next, a deep semantic analysis process develops an identifiable patternfor a malapropism that can be learned and fed into the analysis system:

-   -   Noun, possessive verb, subject followed by laughter, check for        correction, check against facts, and substitute with homophones,        synonyms or frequently misused terms.

Additional methods to detect other types of humor elements may beincluded in the logical processes, as well, such as a method to detect apun (deliberate misuse of a term or a syntactical double entendre), orto detect a polemical statement (use of words associated with politicaldiscussion or with critical statements but followed by an indication oflaughter or amusement).

Segmentation

In one available embodiment, the work of literature is broken intosegments based on the length of the work (e.g. books, chapters,sections), and optionally according to themes (preface, introduction,main body, exercises, worksheets, glossary, index, etc.), depending on apre-analysis of the book which is described in further detail in thefollowing paragraphs. For example, a book may be broken into sectionsroughly of equal sizes (equal page count, equal word count, etc.), basedon the total length of the book, with no more than 4 or 5 sections. Thesections are normalized based on the scale of the book number of pagesand the way the graphical scale should be, not based on the number ofentries for a large book.

The Literary Element Correlation Matcher then can receive the metadataand then scales the information and literary element categories, andmatches these and their importance levels across the sections byinterested literary terms.

Consider this example for a Literary Element for dominant literary genreof “adventure”. A particular section of the book under analysis dealswith discovery of new areas, new items, new techniques and charactergrowth and knowledge increases in a new domain, so metadata (not shownin XML) may consist of:

Adventure Annotation

-   -   Adventure Level—1-10    -   Type (Discovery, Knowledge, Area, Excitement, Danger, Risk)    -   Relationships—Main character(s), Main Plot Element    -   Overall Weight 1-10

A method for identifying and associating important or main charactersis, including but not limited to, by detecting the frequency of thecharacter names or the point of view or perspective of the author. Forexample, most of the main characters' names will occur much morefrequently through the work of literature, and minor or incidentalcharacter names may occur relatively less frequently. One exception maybe a narrator, whose name may appear seldom or not at all, but this canbe determined by detecting that the story is told in a first-personnarrative style. An annotation for each character might appear as such(not shown in XML):

Character Annotation

-   -   Character Importance 1-10    -   Type (Main, Important, Protagonist, Supporting Character)    -   Relationships—Main Character, Main Plot Element    -   Overall Weight 1-10

A method to break the work of literature into segments can beintelligent, such as these example rules or processes:

-   -   break it up based on chapters;    -   break it up based on the character perspective;    -   break it up according to fixed length (e.g. X words, Y pages, Z        sentences) or by proportional length (1/n^(th) of total length        of the work of literature), optionally with breaks rounded to        the nearest significant change (chapter end, page end, paragraph        end, etc.)

An example method for detecting and annotating descriptive detailsaround elements such as environment settings, food, and emotional statesmight employ rules or processes such as:

-   -   analyze sentences for noun and subjects, and identify and        categorize nouns into places, foods, and characters;    -   analyze verbs used in the sentences and whether they are        descriptive verbs, action verbs, or passive verbs;    -   identify adjectives and adverbs in the sentences and their        relationships to the nouns of focus;    -   identify and note the concentration of the descriptive elements        in relation to the nouns and their relationships to the detected        main characters; and    -   note the reactions (mood indicators) of main and important        characters to the descriptive elements (the same type of        reaction should be evoked in the reader—laughing, crying,        surprise, disappointment, bewilderment, etc.).

An example Descriptive Element Annotation may be as follows (notexpressed in XML):

Descriptive Element Annotation

-   -   Description level 1-10 (augmented by type of adjectives and        adverbs)    -   Type (Food, Emotional State, Environment, Culture, Persona)    -   Relationships (Main Character, Environment, Culture, Plot Theme)    -   Overall Weight 1-10

A method for identifying plot devices in a work of literature may be asfollows:

-   -   detect a set of actions taken by a main character or a set of        actions affect main and important characters;    -   assign and categorize the action(s) to one or more events;    -   note and capture instructions or tasks assign to main character        (Quests), which can be by the character or supporting        characters, or they can be driven by the main plot theme;    -   track when actions change main events or when events complete a        task or when plot themes shift; and    -   aggregate these detected plot elements into a set of completed        quests and assign a significance value to the set of items        generating a plot device.

The annotation for plot devices may include (not expressed in XML):

Plot Device Annotation

-   -   Plot Device Level 1-10    -   Type (Travel, Quest Achieved, Character Persona Change, Death,        New Character, New Location)    -   Relationship (Main Character, Important Character, Plot Theme)    -   Overall Weight 1-10        Utility Example

The machine-generated metadata of the present invention may be output toan abstraction analyzer (906), or to an inferential engine fordiscovering deep similarities (907) for the purposes of makingrecommendations via a recommendation engine (908), or to a visualizationgenerator (909, 910), or to any combination of these, as shown in FIG.4.

For the first example of outputting the metadata model to an abstractionanalyzer (906) to illustrate the usefulness of the metadata model, onemay refer to the related U.S. patent application Ser. No. 13/772,017,filed on Feb. 20, 2013 which discloses a method or process ofdecomposing a digital literary piece into deep semantic relationships atvarying levels of abstraction, wherein the first level of abstractioncaptures as many plot elements as possible, and wherein each subsequentlevel represents further abstraction of storyline or plot details, untilhigh level concepts begin to emerge. From this semantic analysis, usersentiment to the literary attributes are inferred and used to identifysimilar literature at varying levels of abstraction and detail.

The method of this particular related invention is advantageous becauseit performs deep semantic analysis of literature at varying levels ofabstraction. Doing so allows two pieces of digital or digitizedliterature to be compared to each other at varying levels of semanticanalysis, offering a deep or shallow comparison as desired. Anadditional benefit of the related method is that the current methodsfocus on shallow semantic analysis, which simply understands similarityof patterns and words as they appear in the text. The disclosed systememploys deep semantic analysis, which analyzes the concepts of the textat a deeper level than pattern and term or key word matching.

Such a method can necessarily benefit from the previously-describedprocesses to detect literary elements, weight them, and relate them tocharacters and to plot devices. The related invention is disclosedherein as an example of how the present invention may be used in atleast one extended application of the new technology.

Suitable Computing Platform

The preceding paragraphs have set forth example logical processesaccording to the present invention, which, when coupled with processinghardware, embody systems according to the present invention, and which,when coupled with tangible, computer readable memory devices, embodycomputer program products according to the related invention.

Regarding computers for executing the logical processes set forthherein, it will be readily recognized by those skilled in the art that avariety of computers are suitable and will become suitable as memory,processing, and communications capacities of computers and portabledevices increases. In such embodiments, the operative invention includesthe combination of the programmable computing platform and the programstogether. In other embodiments, some or all of the logical processes maybe committed to dedicated or specialized electronic circuitry, such asApplication Specific Integrated Circuits or programmable logic devices.

The present invention may be realized for many different processors usedin many different computing platforms. FIG. 3 illustrates a generalizedcomputing platform (500), such as common and well-known computingplatforms such as “Personal Computers”, web servers such as an IBMiSeries™ server, and portable devices such as personal digitalassistants and smart phones, running a popular operating systems (502)such as Microsoft™ Windows™ or IBM™ AIX™, UNIX, LINUX, Google Android™,Apple iOS™, and others, may be employed to execute one or moreapplication programs to accomplish the computerized methods describedherein. Whereas these computing platforms and operating systems are wellknown an openly described in any number of textbooks, websites, andpublic “open” specifications and recommendations, diagrams and furtherdetails of these computing systems in general (without the customizedlogical processes of the present invention) are readily available tothose ordinarily skilled in the art.

Many such computing platforms, but not all, allow for the addition of orinstallation of application programs (501) which provide specificlogical functionality and which allow the computing platform to bespecialized in certain manners to perform certain jobs, thus renderingthe computing platform into a specialized machine. In some “closed”architectures, this functionality is provided by the manufacturer andmay not be modifiable by the end-user.

The “hardware” portion of a computing platform typically includes one ormore processors (504) accompanied by, sometimes, specializedco-processors or accelerators, such as graphics accelerators, and bysuitable computer readable memory devices (RAM, ROM, disk drives,removable memory cards, etc.). Depending on the computing platform, oneor more network interfaces (505) may be provided, as well as specialtyinterfaces for specific applications. If the computing platform isintended to interact with human users, it is provided with one or moreuser interface devices (507), such as display(s), keyboards, pointingdevices, speakers, etc. And, each computing platform requires one ormore power supplies (battery, AC mains, solar, etc.).

CONCLUSION

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, steps, operations, elements, components, and/or groupsthereof, unless specifically stated otherwise.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

It should also be recognized by those skilled in the art that certainembodiments utilizing a microprocessor executing a logical process mayalso be realized through customized electronic circuitry performing thesame logical process(es).

It will be readily recognized by those skilled in the art that theforegoing example embodiments do not define the extent or scope of thepresent invention, but instead are provided as illustrations of how tomake and use at least one embodiment of the invention. The followingclaims define the extent and scope of at least one invention disclosedherein.

What is claimed is:
 1. A method comprising: creating one or more digitalannotations, by a computer processor, using natural language processingand deep semantic processing, for one or more non-plot device literaryelements interrelated with one or more plot devices within a literaryplot in a digital work of literature, wherein the one or more plotdevices are distinguished from a general theme and a general plot;assigning, by a computer processor, weights to the one or more createdannotations according to an importance level and a relevance level ofeach created annotation as determined by computer-performed deepsemantic analysis; identifying, by a computer processor, a shift withinthe literary plot for one or more of the plot devices; augmenting, by acomputer processor, the importance level and the relevance levelassociated with each respective annotated interrelationship to yield anoverall weight for each of the interrelationships before, after orbetween the shift within the literary plot; and producing, by a computerprocessor, an output depiction, on a user interface device, theannotations, the interrelationships, and the overall weights.
 2. Themethod as set forth in claim 1 further comprising assigninginterrelationships and type characteristics by correlating and matchingthe non-plot device literary elements and the plot devices, and whereinthe augmenting to yield the overall weight comprises assigning overallweights to one or more correlated and matched literary elements.
 3. Themethod as set forth in claim 2 wherein the correlating and matchingcomprises assigning, by a computer processor, to each literary element,one or more attributes selected from the group consisting of level ofimportance within the segment, level of prevalence within the segment,number of parties in a relationship to which the literary elementpertains in the segment, and number of plot devices in the segment towhich the literary element pertains.
 4. The method as set forth in claim3 wherein the correlating and matching further comprises calculating asegment-bound overall weight for a literary element within the segmentby applying a mathematical weighting function to the one or moreattributes.
 5. The method as set forth in claim 4 wherein thecorrelating and matching further comprises calculating an overall weightfor a literary element within a plurality of segments by applying amathematical weighting function to one or more segment-bound overallweights.
 6. The method as set forth in claim 3 wherein the correlatingand matching further comprises calculating an overall weight for aliterary element within the entire digital work of literature byapplying a mathematical weighting function to the one or moreattributes.
 7. The method as set forth in claim 1 wherein the createdannotations include one or more annotations selected from the groupconsisting of humor, imagery, adventure, character, character mood, andsuspense.
 8. The method as set forth in claim 7 wherein the humorannotation is further classified as a type of humor selected from thegroup consisting of malapropism, pun, polemics, dark, silly, political,genderist, sarcasm, epigrammatic, exaggeration, irony, situational, andsatire.
 9. The method as set forth in claim 1 wherein at least oneliterary element annotation weight is normalized across a category inwhich the literary element has been categorized.
 10. The method as setforth in claim 1 wherein at least one literary element weight isnormalized across a segment of the digital work of literature in whichthe literary element is found.
 11. The method as set forth in claim 1further comprising automatically breaking, by a computer system, thedigital work of literature into segments.
 12. The method as set forth inclaim 11 wherein the breaking into segments is performed according toone or more rules selected from the group consisting of proportionallydividing the digital work of literature into a fixed number of segmentsand dividing the digital work of literature into segments according tofixed lengths of the segments.
 13. A computer program productcomprising: a tangible, computer readable memory device; and programinstructions encoded by the computer readable memory device for causingone or more processors to perform operations of: creating one or moredigital annotations, using natural language processing and deep semanticprocessing, for one or more non-plot device literary elementsinterrelated with one or more plot devices within a literary plot in adigital work of literature, wherein the one or more plot devices aredistinguished from general theme and general plot; assigning weights tothe one or more created annotations according to an importance level anda relevance level of each created annotation as determined bycomputer-performed deep semantic analysis; identifying a shift withinthe literary plot for one or more of the plot devices; augmenting theimportance level and the relevance level associated with each respectiveannotated interrelationship to yield an overall weight for each of theinterrelationships before, after or between the shift within theliterary plot; and producing an output depiction, on a user interfacedevice, the annotations, the interrelationships, and the overallweights.
 14. The computer program product as set forth in claim 13wherein the encoded program instructions are further for assigninginterrelationships and type characteristics by correlating and matchingthe non-plot literary elements and the plot devices, and wherein theaugmenting to yield the overall weight comprises assigning overallweights to one or more correlated and matched literary elements.
 15. Thecomputer program product as set forth in claim 13 wherein the encodedprogram instructions are further for: automatically breaking the digitalwork of literature into segments according to one or more rules selectedfrom the group consisting of proportionally dividing the digital work ofliterature into a fixed number of segments and dividing the digital workof literature into segments according to fixed lengths of the segments;calculating a segment-bound overall weight for a literary element withinthe segment by applying a mathematical weighting function to the one ormore attributes; and calculating an overall weight for a literaryelement within a plurality of segments by applying a mathematicalweighting function to one or more segment-bound overall weights.
 16. Acomputer system comprising: a processor and a tangible, computerreadable memory device; and program instructions encoded by the computerreadable memory device for causing the processor to perform operationsof: creating one or more digital annotations, using natural languageprocessing and deep semantic processing, for one or more non-plot deviceliterary elements interrelated with one or more plot devices within aliterary plot in a digital work of literature, wherein the one or moreplot devices are distinguished from general theme and general plot;assigning weights to the one or more created annotations according to animportance level and a relevance level of each created annotation asdetermined by computer-performed deep semantic analysis; identifying ashift within the literary plot for one or more of the plot devices;augmenting the importance level and the relevance level associated witheach respective annotated interrelationship to yield an overall weightfor each of the interrelationships before, after or between the shiftwithin the literary plot; and producing an output depiction, on a userinterface device, the annotations, the interrelationships, and theoverall weights.
 17. The computer system as set forth in claim 16wherein the encoded program instructions are further for assigninginterrelationships and type characteristics by correlating and matchingthe non-plot literary elements and the plot devices, and wherein theaugmenting to yield the overall weight comprises assigning overallweights to one or more correlated and matched literary elements.
 18. Thecomputer system as set forth in claim 16 wherein the encoded programinstructions are further for: automatically breaking the digital work ofliterature into segments according to one or more rules selected fromthe group consisting of proportionally dividing the digital work ofliterature into a fixed number of segments and dividing the digital workof literature into segments according to fixed lengths of the segments;calculating a segment-bound overall weight for a literary element withinthe segment by applying a mathematical weighting function to the one ormore attributes; and calculating an overall weight for a literaryelement within a plurality of segments by applying a mathematicalweighting function to one or more segment-bound overall weights.